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An Investigation into the Poor Survival of an Endangered Coho Salmon Population Cedar M. Chittenden1,2*, Michael C. Melnychuk3, David W. Welch4, R. Scott McKinley2 1 Department of Arctic and Marine Biology, University of Tromsø, Tromsø, Norway, 2 The University of British Columbia Centre for Aquaculture and Environmental Research, West Vancouver, British Columbia, Canada, 3 Fisheries Centre and Department of Zoology, The University of British Columbia, Vancouver, British Columbia, Canada, 4 Kintama Research Corporation, Nanaimo, British Columbia, Canada

Abstract To investigate reasons for the decline of an endangered population of coho salmon (O. kisutch), 190 smolts were acoustically tagged during three consecutive years and their movements and survival were estimated using the Pacific Ocean Shelf Tracking project (POST) array. Median travel times of the Thompson River coho salmon smolts to the lower Fraser River sub-array were 16, 12 and 10 days during 2004, 2005 and 2006, respectively. Few smolts were recorded on marine arrays. Freshwater survival rates of the tagged smolts during their downstream migration were 0.0–5.6% (0.0–9.0% s.e.) in 2004, 7.0% (6.2% s.e.) in 2005, and 50.9% (18.6% s.e.) in 2006. Overall smolt-to-adult return rates exhibited a similar pattern, which suggests that low freshwater survival rates of out-migrating smolts may be a primary reason for the poor conservation status of this endangered coho salmon population. Citation: Chittenden CM, Melnychuk MC, Welch DW, McKinley RS (2010) An Investigation into the Poor Survival of an Endangered Coho Salmon Population. PLoS ONE 5(5): e10869. doi:10.1371/journal.pone.0010869 Editor: Bjo¨rn Brembs, Freie Universitaet Berlin, Germany Received January 26, 2010; Accepted April 28, 2010; Published May 28, 2010 Copyright: ß 2010 Chittenden et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Funding for this work was provided by Census of Marine Life, the Gordon and Betty Moore Foundation, and the Pacific Salmon Foundation. The Pacific Ocean Shelf Tracking project (POST) acoustic receiver arrays were designed and deployed by Kintama Research Corp. None of these organizations were directly involved in the study design, analysis, or writing of the paper, nor the decision to submit the manuscript for publication. This work is a contribution to the Census of Marine Life. Competing Interests: Dr. David Welch is the president of Kintama Research. Kintama Research is an environmental consultancy that designed and deployed large parts of the current array. Their operating costs come from the Gordon and Betty Moore Foundation. Neither of these organizations were directly involved in the study design, analysis, or writing of the paper, nor the decision to submit the manuscript for publication. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors. * E-mail: [email protected]

Canadian salmon populations became endangered after 1989, including the Thompson coho [18]. Coho salmon production off the west coast of North America has varied with latitude, however. While the southern populations dwindled, northern populations of coho salmon and steelhead trout had record-high abundances [2], [19], [20]. In addition to altering salmonid marine survival rates, ocean conditions have been correlated to changes in the migratory pattern of Strait of Georgia coho populations [4], [16] (which the Thompson coho migrate into after exiting the Fraser River) and other salmon species [21]–[24]. The Strait of Georgia once supported large commercial and recreational coho fisheries (valued at CDN $218.5 million [25]). During the mid-1990s the Strait of Georgia coho fishery collapsed, likely due to a decline in marine survival and a complete shift in migratory behaviour [16]. The Thompson coho, one of the primary Strait of Georgia coho populations, historically spent the marine phase of their life cycle almost entirely within the Strait of Georgia [26]. Since 1995, however, nearly all of the coho left the Strait by the February of their first ocean winter [27]. The cause of this behavioural change is not known, but as with most recent changes in salmon survival and behaviour, the leading hypothesis attributes it to shifting ocean productivity [4]. The Strait of Georgia has had an increasing average sea surface temperature as well as other oceanographic trends attributed to the changing climate [4].

Introduction Many salmon populations are in decline worldwide [1]–[3], with low marine survival identified as the primary cause [4], [5]. In this study we report specific results that suggest a freshwater problem during the coho smolt migration is also a contributing factor in the Thompson River system of British Columbia (BC; Fig. 1). Genetically distinct from coho salmon (Oncorynchus kisutch) populations in the lower Fraser River and the rest of BC, Thompson River coho salmon (Thompson coho) are closely related to the extinct upper Columbia River coho populations [6]–[8]. Returns of wild Thompson coho were relatively stable during the 1970s, and increased during the 1980s [3]. However, between 1988 and 2000, the Thompson coho population declined by 90%, making it one of Canada’s most endangered salmon populations [3], [9]. An unprecedented moratorium on the west coast salmon fishery was implemented in 1998, but the Thompson coho population did not recover [10]. Thus over-fishing alone did not prevent the recovery of the population. An urgent request for investigations into the cause of the Thompson coho’s demise was made shortly thereafter by the government of Canada [11]. The overall marine survival of coho salmon in southern BC has declined during the past three decades [10], [12]–[15]. When a regime shift occurred in 1989–1990, ocean productivity decreased in southern BC and smolt-to-adult marine survival of both coho [16], [17] and steelhead trout (O. mykiss) plummeted [1], [2]. Four PLoS ONE | www.plosone.org

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May 2010 | Volume 5 | Issue 5 | e10869

Tracking Endangered Coho

Figure 1. Geographic location of part of the POST acoustic array and smolt release sites. The edge of the continental shelf (200 m depth contour) is shown, as well as acoustic listening lines located in the Fraser River, Strait of Juan de Fuca (JDF), the northern Strait of Georgia (NSOG), and the Queen Charlotte Strait (QCS). Thompson River coho salmon smolts were released at 1) Spius Creek and 2) the Coldwater River. doi:10.1371/journal.pone.0010869.g001

In addition to the trends observed in the marine environment, freshwater productivity and habitat quality are also affected by changes in climate [28]. However, there is a lack of quantitative evidence concerning the effects of climate change on the freshwater production of coho salmon smolts [29]. Recent climate trends in the North Pacific have been correlated to higher temperatures and earlier spring flows in the Fraser River system [30], which seems to be advancing the out-migration timing of wild coho smolts [27]. In the Thompson River system, an increasing average April flow rate [31]—indicating an earlier spring freshet—may be affecting the early freshwater survival and behaviour of wild and hatchery Thompson coho smolts. Decreased water quality during high flow periods [31] is also a major concern for coho salmon in the Thompson watershed. Rood and Hamilton [32] found that during the summer months in this semi-arid valley, large amounts of water are being withdrawn from the Thompson River for irrigation purposes, lowering flows and increasing mean summer temperatures. As coho spend their first year in the river environment, they are especially sensitive to flow levels, temperature extremes, siltation, predation, and disease [33]. Optimal survival conditions for coho smolts were found to be in rivers with relatively cooler temperatures, deep pools, structurally complex habitats, intermediate second winter flows, and high second spring flows [34]. Fluctuations in the abundance of Thompson coho were weakly correlated to agricultural land use, road density and stream habitat quality [10]. Concerns have been expressed repeatedly about the freshwater habitat quality in the Thompson River watershed, as well as the need for coho riverine-survival data that could provide evidence linking weak populations to freshwater habitat concerns [9], [11]. Over-fishing, changes in ocean climate and a basin-wide deterioration of freshwater habitat are believed to be the primary causes of the Thompson coho decline [10]. To investigate smolt survival rates as well as the migratory behaviour of Thompson coho smolts, hatchery-reared fish were tagged during three consecutive years (2004–2006) and monitored by the Pacific Ocean Shelf Tracking project (POST) array [35]. The objective of this study was to identify areas of high mortality for this population PLoS ONE | www.plosone.org

during their early migration phase, with the goal of providing a focus for conservation efforts. A two-year tag effects study was carried out concurrently to evaluate the post-surgical growth, survival, tag retention, health and swimming ability of acoustically tagged smolts [36].

Methods Ethics Statement All work involving live fish reported in this paper was annually reviewed and pre-approved as meeting or exceeding the standards laid out by the Canadian Council on Animal Care. In 2004 and 2005 the project guidelines were approved by the Department of Fisheries and Oceans Canada Pacific Region Animal Care Committee. In 2006 review and approvals were made by the Animal Care Committee of Malaspina University-College (now Vancouver Island University), c/o Patricia Stuart, Vancouver Island University, Nanaimo, BC, Canada.

Study Area Near the end of the last ice age approximately 15,000 years ago, the Fraser River canyon was blocked by ice, forcing the upper Fraser and Thompson Rivers to drain southward into the Columbia River. This provided the opportunity for many species, including coho salmon, to colonize the Thompson River from the Columbia River refugium [37]. The Fraser canyon continues to act as a velocity barrier to many fish species and populations, dividing the Upper and Lower Fraser River into distinct habitat zones and genetically distinct coho populations. Genetic data indicate that there has been almost no interchange between upper and lower Fraser River coho populations during the past 10,000 years [38]. Thompson coho populations can be divided into three sub-regions: the North Thompson, the South Thompson and the Lower Thompson/Nicola. The populations studied here were from the Lower Thompson/Nicola group. The 1,370 km long Fraser River (Fig. 1) is the largest river in BC, with a watershed of 233,100 km2, and an average yearly flow rate of 3,540 m3?s21. The Thompson River, at 489 km long, is the 2



had an average fork length of 12864 mm (125–139 mm). They were released on 19 May 2005 at 1115 PDT in Spius Creek to coincide with the hatchery release of 58,450 coho smolts. The 2004 brood of the Coldwater River population was tagged in 2006 from 25–26 May with V7-2L transmitters. One hundred smolts were implanted with V7-2L transmitters and released 29 May 2006, at 1300 PDT. The average fork length of the tagged smolts was 13063 mm (125–141 mm). The river temperature during the surgeries was 8.260.9uC. The other hatchery releases were as follows: 43,000 smolts were released 9 May 2006, 20,000 smolts were released 26 May 2006, and 6,460 smolts were released 29 May 2006 in the Coldwater River (Fig. 1) at the time of the release of the tagged smolts. Tag effect studies on 500 Coldwater River coho smolts during 2005 and 2006 demonstrated that the implantation of V7-2L tags did not impact fish survival or swimming ability at the body sizes used in the field study when compared with control groups [36]. Physiological assessments, swimming performance and growth were all similar to control values [36]. Smolts with an initial fork length $125 mm (the minimum size used in this study) tagged with V7-2L transmitters had 100% tag retention and survival over a 300 day period [36]. The larger V9-6L tags implanted in coho smolts of the size range tagged in the field study (130–141 mm) had 92–100% survival and tag retention three weeks after tagging [36]. Monitoring of the achieved life-span of V9-6L and V7-2L tags deliberately held back and monitored in the laboratory indicates essentially 100% operation to 90 days post-activation (Welch, unpublished data), which is far longer than the migration times measured in this study (ca. two weeks).

largest tributary of the Fraser River, with a watershed of 55,400 km2. Water quality and flow rate monitoring of the Thompson River began in 1911 at the Spences Bridge station, 40 km upstream from where the Thompson River enters the Fraser River. Average annual flow rates during 2004, 2005, and 2006 were 672, 784 and 656 m3?s21 respectively, which fall near the 1912–2006 annual average of 765 m3?s21 [31]. Peak flow rates were 1,820 m3?s21 on 4 June 2004, 2,230 m3?s21 on 19 May 2005, and 2,630 m3?s21 on 27 May, 2006. Low flow rates were 171 m3?s21 on 4 March 2004, 250 m3?s21 on 17 June 2005, and 193 m3?s21 on 28 October 2006 [31]. The timing of the spring freshet in the Fraser River has been advancing during the past century [16], and a similar trend has been observed in the Thompson River [31]. The Thompson River water quality is well-buffered and soft, with pH and oxygen levels ‘‘within normal ranges for aquatic life’’ [39]. However, levels of non-filterable residue, turbidity, total aluminum, iron and phosphorus often exceed recommended levels for fish during the spring freshset [31]. There was an increasing trend from 1973–1997 in dissolved chloride and copper [39]. The minimum detectable limit for copper, however, was higher than the limit for aquatic life (0.002 mg?L21) [31]. Non-filterable residue and turbidity level increases have been attributed to increased agriculture, forestry and residential development [39]. The temperature of the South Thompson River during the spring freshet is generally not a problem for salmon [31]. However, the mean summer temperatures of the entire Fraser River system have been increasing over the last century; during the next 100 years, the potential for salmon to be exposed to temperatures higher than 20uC is predicted to increase by a factor of ten [40]. Most Thompson coho salmon spend their first year-and-a-half in freshwater and the following two years in the ocean, before returning to their natal streams to spawn [10]. Hatchery production was initiated in the Thompson watershed in the early 1980s to test enhancement strategies for coho [41], [42]. Smolt production began at the Spius Creek Hatchery in 1984 to rebuild depressed populations. Assessments of returns for both wild and hatchery populations were carried out regularly thereafter. The hatchery coho broodstock is collected from the wild population each year.

Acoustic receiver array Acoustic receivers (models VR2 and VR3, VEMCO Ltd, Halifax, Nova Scotia Canada) were located both in the Fraser River and in the ocean (forming the POST array; Fig. 1) to track the smolts’ downstream migration. A summary of the Fraser River sub-line locations is available in Welch et al. [46]. Briefly, the total distance from the release site to the mouth of the Fraser River is approximately 385 km from the 2005 release site and 420 km from the 2004 and 2006 release sites. Distances from release sites to the last detection station in the Fraser River were 381 km, 353 km and 410 km during 2004, 2005 and 2006, respectively. During 2004, there were six receivers in the Fraser River arranged in three lines of paired units from April to August, then five receivers from August to November. Four receivers (two lines of paired units) were deployed in the Fraser River in April of 2005 and recovered in December. Eighteen receivers were deployed in the Fraser River from April to December of 2006. These were arranged in three main lines, where the lower two lines contained two or three sub-lines each to cover multiple channels of the braided lower river (Fig. 1). In ocean waters, acoustic receivers were positioned to form sublines extending across the northern Strait of Georgia, Queen Charlotte Strait, Juan de Fuca Strait, and Howe Sound (Fig. 1). Also, in 2006, receivers were moored in Burrard Inlet and the southern Strait of Georgia. During 2005 and 2006, three receivers operated by the Vancouver Aquarium were located at Point Atkinson (Burrard Inlet and Point Atkinson are located in the ocean near the Fraser River mouth; for exact locations, see www. postcoml.org).

Surgical Protocols and Smolt Releases Coho smolts from two different tributaries—the Coldwater River (2004, 2006) and Spius Creek (2005)—were tagged at the Spius Creek Hatchery using previously established protocols [43], [44]. During 2004, 40 Coldwater River coho smolts from the 2002 brood were implanted with V7-2L acoustic transmitters (12 fish; 7618.5 mm, mass in air 1.4 g, mass in water 0.7 g, frequency 69 kHz, 30–90 s random delay, VEMCO Ltd, Halifax, Nova Scotia Canada), and V9-6L acoustic transmitters (28 fish; 9620 mm, mass in air 3.3 g, mass in water 2.0 g, frequency 69 kHz, 30–90 s, VEMCO Ltd, Halifax, Nova Scotia Canada). The surgeries were carried out 30 May 2004. The average fork length (6SD) of the fish tagged with 7 mm tags was 12861 mm (range 127–129 mm), and for those with 9 mm tags, 13262 mm (130–141 mm). The river temperature was 11.560.6uC during the surgeries. The release dates of 88,300 hatchery coho smolts were from 19–22 May 2004; the tagged fish were released 31 May 2004 at 1400 PDT in the Coldwater River (Fig. 1). For a comparison of hatchery and wild smolt-to-adult survival rates of Pacific salmon, see Walters and Ward [45]. During 2005, 50 Spius Creek coho smolts from the 2003 brood were implanted with V7-2L tags. The river temperature averaged 9.060.7uC on the day of the surgeries, 17 May 2005. The smolts PLoS ONE | www.plosone.org

Data Analysis We compiled a database of detections from acoustic receivers consisting of the time and location where an individual tag was detected. First, we identified a list of suspect detections likely to be 3



population. For further experimental details about the other Fraser River populations, see Welch et al. [46]. To determine whether survival or detection probabilities were best described as functions of factors such as tag size, river flow, release day, or average travel time, we considered several candidate models. We combined all three years in a detection history dataset and assigned ‘year’, ‘species’ and ‘population’ as group covariates on survival probability estimates. Combining years allowed us to constrain the relative difference in p between V7 tags and V9 tags to be consistent (in logit-space) across receiver lines and years (i.e., tag size was an additive covariate). In some models, it also allowed us to maintain a consistent relationship (slope) between either w or p and a model covariate (day of year, river level, or travel time) across lines and years. Although parameter estimates were related through the slopes of such covariates in some models, the intercepts were permitted to differ, thereby maintaining some independence in parameter estimates among lines, years, species, and populations. We considered six candidate models for p, comparing them with information-theoretic criteria [53]. In this comparison we assumed a common sub-model for w, where parameter estimates for each segment (‘seg’, or ‘time’ in usual nomenclature) and group (year, species, tag type, population) varied freely, i.e., w(seg6G). These sub-models of p were: (i) p(line6G); (ii) p(line6year); (iii) p(line6year+tag type); (iv) p(line6year+tag type+day of year); (v) p(line6year+tag type+flowMission); and (vi) p(line6year+tag type+flowPort Mann). Submodel (i) had freelyvarying parameters for each detection line (i.e., replacing ‘time’) and group (along with w, this is the classic CJS model). All other sub-models also maintained independence between different lines and years, i.e., p(line6year…). One of these (ii) assumed no difference in detection probability between V7 and V9 tags while the others (iii–vi) assumed an additive difference that was consistent among years, i.e., p(…+tag type…); Table 1). Three of these sub-models took into account the mean day of arrival of a population on a receiver line and assumed that p at each line was a function of day-of-year or water level at that particular mean arrival day, thereby allowing for variation in p among populations through use of these covariates. Water level (which is correlated with river flow) was measured at either the Mission (near the first receiver line in 2004; Fig. 1) or Port Mann (near the second receiver line in 2005) gauge stations [31]. One of these sub-models involved the day-of-year (iv) of arrival at a line as a covariate, another involved the water level at Mission (v), and a third involved the water level at Port Mann at the mean arrival time at a line (vi). Thus, these three sub-models (iv–vi) involved relationships with additive covariates that constrained the effect of the covariate on p to be similar among populations and species, but p estimates still differed by way of populations and species having their own particular values of the covariate at each receiver station. After using model selection methods to identify the best submodel for p, we held this sub-model fixed in order to compare four candidate sub-models of w: (a) w(seg6G); (b) w(seg6G+release day of year); (c) w(seg6G+flowMission); and (d) w(seg6G+TravelTime). This two-step process of first comparing hypotheses of p submodels before comparing hypotheses of w sub-models has been used in several other studies (e.g. [54]). To reduce the effect of incorporating other species and populations into the same dataset on w estimates of Thompson coho, we maintained independence between groups and segments in all four sub-models, w(seg6G…). One of these sub-models (a) contained no extra covariates so represented complete independence in w estimates between groups in each segment of the migration. The other three sub-models

false positives due to tag code collisions (when the signals from two or more tags interfere to cancel each other out or create false tag codes) or other noise sources. Detections of fish were excluded as false if they were detected only once on a line within 60 minutes, had one or more tags heard on the same receiver around the time of the suspect detection, and did not have supporting detections from other time periods or lines. Supporting detections are defined as a temporal sequence of detections from the release date along the migration path. After eliminating the suspect detections, we used these filtered data to estimate survival and detection probabilities as well as the travel times of tagged smolts during the downstream migration. Travel times in each segment were measured as the difference between successive lines in the cumulative travel times from release until the first detection of a tag on a line. Median travel times were calculated as the linearly interpolated time at which 50% of the survivors reached a detection point.

Survival probability estimation We used variations of the fully time-varying Cormack-JollySeber (CJS) mark-recapture model for live recaptures to estimate survival probabilities (w) in each segment of the downstream migration [47]–[49]. This model simultaneously estimates detection probabilities (p) at each line of receivers in the Fraser River and adjusts survival estimates accordingly. We determined the detection history of individual fish at each receiver (i.e. ‘‘recapture’’) line. Fraser River salmon smolts migrated past 2–3 detection lines in the Fraser River (depending on year). There were multiple receiver lines or units in the ocean where they could be detected (with some variation among years), but typical migration routes after ocean entry could not be established because few fish were detected on ocean receivers. As a result, we lumped all ocean receiver detections into the final digit of a fish’s detection history sequence, and limited our inferences of survival to the downstream migration phase and not to the early ocean migration (i.e., we disregard any estimates of the confounded parameters w in the final ocean ‘‘segment’’ and p at the final ocean ‘‘line’’). Detections of Thompson coho were relatively few on river or ocean lines, when compared to detection data for Thompson spring Chinook salmon (O. tshawytscha) and steelhead trout [46], and Cultus Lake sockeye salmon (O. nerka) [50]. Therefore, we used information from smolts of other Fraser River populations tagged as part of the POST project to better estimate p on river lines. Combining populations and species in the same analysis had the following three advantages: (1) sample sizes of detected coho at receiver stations were often small, so using information from other species can result in more reliable parameter estimates; (2) it allows for a common relationship between model parameters (especially p) and environmental covariates like river level or day of year, since this relationship is expected to be consistent across populations; and (3) it allows for a common effect of tag type on p across populations. We assume that the same tag type (and therefore acoustic power) passing over a river receiver line around the same time has the same probability of being detected regardless of the species or population from which the tagged smolts originated (apart from run-timing differences between populations; see below). We constructed similar detection histories for steelhead trout and Chinook salmon smolts released in the Thompson watershed, and for sockeye salmon smolts released from Cultus Lake. We used the detection history sequences of individual fish with various mark-recapture models implemented in Program MARK [51] through RMark [52] to estimate w inriver segments as well as p on river lines in each year for each PLoS ONE | www.plosone.org

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Table 1. Model selection results for recaptures-only survival (w) and detection probability (p) estimates.a

np

22?ln(L)

QAICc

DQAICc

Akaike weight

w(seg|G) p(line|yearztag typezflowMission) d

94

3 502.7

2 384.8

0.0

0.98

w(seg|G) p(line|yearztag typezflow Port Mann) d

94

3 515.4

2 392.8

8.0

0.02

Model Detection probability sub-modelsb

w(seg|G) p(line|yearztag typezday of year)

d

94

3 528.5

2 400.9

16.1

0.00

w(seg|G) p(line|G)

160

3 379.4

2 454.8

70.0

0.00

w(seg|G) p(line|yearztag type)

93

3 633.1

2 464.1

79.3

0.00

w(seg|G) p(line|year)

92

3 688.7

2 496.7

111.9

0.00

w(seg|Gzrelease day of year) p(:::)

95

3 495.6

2 382.6

0.0

0.65

w(seg|G) p(:::)

94

3 502.7

2 384.8

2.3

0.21

95

3 502.7

2 387.0

4.4

0.07

95

3 502.7

2 387.0

4.4

0.07

Survival probability sub-modelsc

w(seg|Gzflow Mission) p(:::)

e

w(seg|GzTravelTime) p(:::) a

Quantities shown are the number of parameters (np), log-likelihoods, QAICc values (adjusted for small sample sizes and extra-binomial variation with ^c = 1.60), and Akaike weights. Sub-models for p are compared while the fully time- (‘‘seg’’) and group-varying CJS sub-model for w is held constant, w(seg6G) . Groups consist of separate combinations of species, population, tag type, and year. c Sub-models for w are compared while the sub-model for p is held constant at the best model from the above model set, p(line6year+tag type+flow Mission). d Flow covariate terms specify that p estimates on receiver lines are dependent upon the river water level measured at either Mission or Port Mann guage stations [31] or upon day of year. See text. e The flow covariate term specifies that w estimates in river segments are dependent upon the river water level measured at the Mission guage station at the start of each segment. See text. doi:10.1371/journal.pone.0010869.t001 b

involved group covariates that were used to explain some of the among-population variation in survival probabilities in terms of variables specific to each population; these could reveal potential correlates of survival that would not be possible by considering Thompson coho alone. One sub-model (b) involved the average release day-of-year as a covariate on all river segments of the migration (all coho populations had only a single release day each year, but some populations of other species were released at two or more periods). Another sub-model (c) involved the average travel time of the population within each segment. The last sub-model (d) involved the water level at the Mission gauge at the start time of each segment of each population (the time of fish release for the first segment and the mean time of arrival at a receiver line for the second or third segments). We estimated a variance inflation factor (^c) to compensate for extra-binomial variation in estimated probabilities [55]. We estimated ^c assuming the general CJS model, w(seg6year), p(line6year), using two methods through Program MARK: the deviance ratio bootstrapping method (^c = 1.600) and median-^c method (^c = 1.28160.037 s.e.) We used the larger value from the bootstrapping routine to be more conservative about the precision of estimated parameters, as these ^c values were used to expand standard errors of real parameter estimates and values in the variance-covariance matrix. Estimated ^c was also used for model comparison, with computed QAICc values corrected for both extra-binomial variation and small sample sizes. After the best sub-model for p was identified and sub-models for w were compared, we computed model-averaged parameter estimates for w in each segment for each population and year. We calculated survivorship estimates from release until the last inriver detection line in each year as the product of segment-specific w estimates. We used the Delta method to calculate the variance of this product. PLoS ONE | www.plosone.org

Results Model selection Of the six detection probability sub-models evaluated across all years, the strongest support by far was found in sub-model (v), which involved line- and year-specific estimates with an additive term for tag size and an additive term for river level at the Mission gauge during the mean time of arrival of populations on receiver lines (Table 1). The difference in DQAICc values between this and the next-best sub-model was fairly large (